US_UrbanSchool_Retention

[Harsha-chandaluri]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @Harsha-chandaluri, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request establishes a comprehensive and automated data pipeline for US Urban School Retention rates. The pipeline is designed to fetch, clean, and transform raw data from the Civil Rights Data Collection, making it ready for integration and analysis. This initiative aims to provide valuable insights into student retention trends across various demographic groups and grade levels.

Highlights

• New Data Import: Introduced a new data import pipeline for US Urban School Retention rates, covering student retention from grades 1 to 12, categorized by gender, race/ethnicity, disability status, and language fluency.
• Automated Data Workflow: Implemented a Python script (download_script.py) to automatically download and preprocess data from the Civil Rights Data Collection (CRDC), including adding year and NCESID, converting 'Yes'/'No' values, and numeric conversions.
• Import Configuration: Added necessary configuration files (manifest.json, retention_metadata.csv, retention_pvmap.csv) to define the import specifications, statistical variables, and mapping rules for the retention data.
• Monthly Automation: The entire import pipeline is configured to run automatically on a monthly schedule, ensuring up-to-date retention data.

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Code Review

This pull request introduces a new data import for US school retention statistics. The changes include a Python script for downloading and preprocessing data, along with necessary configuration files and documentation. The overall structure is good, but there are several areas that need improvement. The download script could be made more robust and maintainable by addressing code duplication and improving error handling. More importantly, there are critical errors in the configuration files, such as a malformed metadata CSV and incorrect schema values in the PV map, which will prevent a successful import.